System Parameter Identification
Information Criteria and AlgorithmsEdited by
- Badong Chen, University of Florida, Gainesville, USA
- Yu Zhu, Tsinghua University, Beijing, China
- Jinchun Hu, Tsinghua University, Beijing, China
- Jose Principe, University of Florida, Gainesville, FL, USA
Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors research provides a base for the book, but it incorporates the results from the latest international research publications.
Engineers, scientists and graduate students interested in information theory, signal processing, system identification and adaptive system training.
Hardbound, 266 Pages
Published: August 2013
Introduction: system identification and criteria
Chapter 2Main Information theoretic measures and Their properties
Chapter 3Information theoretic parameter estimation
Chapter 4System parameter identification: minimum error entropy criterion
Chapter 5System parameter identification: minimum information divergence criterion
Chapter 6System parameter identification: mutual information criterion